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This repository contains one of my Google Sheet files and a conference paper that has been accepted in ISPA IEEE, The Sheet used for organizing research papers related to breast cancer analysis. The focus of the papers is on the utilization of clinical datasets and machine/deep learning techniques. The collection spans the period from 2020 to 2023.

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NadiaAzri/Research-Papers-Breast-Cancer-Clinical-Dataset

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Research Papers Organization - Breast Cancer Analysis (2020-2023)

Overview

This repository contains one of my Google Sheet files, it used for organizing research papers related to breast cancer analysis. The focus of the papers is on the utilization of clinical datasets and machine/deep learning techniques. The collection spans the period from 2020 to 2023.

Contents

Structure of the Google Sheet

The Google Sheet is organized into different sections to facilitate efficient literature management:

  1. Publication Details: Includes paper title, authors, publication date, source, and URL of the paper.

  2. Clinical Datasets: Highlights the datasets used in the research, providing insight into the clinical aspects of breast cancer.

  3. ML/DL Techniques: Summarizes the machine learning and deep learning techniques employed in the analyzed papers.

  4. Key Findings: A section for recording the key findings and contributions of each paper.

How to Use

Feel free to explore the Google Sheet to gain insights into the latest research on breast cancer analysis using clinical datasets and machine/deep learning techniques. The organization is designed to assist researchers, practitioners, and enthusiasts in navigating through relevant literature.

Contributing

If you come across additional papers or have suggestions for improvement, please consider contributing to this file.

About

This repository contains one of my Google Sheet files and a conference paper that has been accepted in ISPA IEEE, The Sheet used for organizing research papers related to breast cancer analysis. The focus of the papers is on the utilization of clinical datasets and machine/deep learning techniques. The collection spans the period from 2020 to 2023.

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